h1

h2

h3

h4

h5
h6
000814711 001__ 814711
000814711 005__ 20250106102620.0
000814711 0247_ $$2ISSN$$a0098-8847
000814711 0247_ $$2ISSN$$a1096-9845
000814711 0247_ $$2SCOPUS$$aSCOPUS:2-s2.0-85101777599
000814711 0247_ $$2WOS$$aWOS:000622176100001
000814711 0247_ $$2datacite_doi$$a10.18154/RWTH-2021-02288
000814711 0247_ $$2doi$$a10.1002/eqe.3432
000814711 037__ $$aRWTH-2021-02288
000814711 041__ $$aEnglish
000814711 082__ $$a550
000814711 1001_ $$0P:(DE-82)IDM04268$$aThaler, Denny$$b0$$urwth
000814711 245__ $$aMachine‐learning‐enhanced tail end prediction of structural response statistics in earthquake engineering$$honline, print
000814711 260__ $$aNew York, NY [u.a.]$$bWiley$$c2021
000814711 3367_ $$00$$2EndNote$$aJournal Article
000814711 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal
000814711 3367_ $$2BibTeX$$aARTICLE
000814711 3367_ $$2DRIVER$$aarticle
000814711 3367_ $$2DataCite$$aOutput Types/Journal article
000814711 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000814711 588__ $$aDataset connected to CrossRef
000814711 591__ $$aGermany
000814711 7001_ $$0P:(DE-82)IDM01173$$aStoffel, Marcus$$b1$$urwth
000814711 7001_ $$0P:(DE-82)IDM01243$$aMarkert, Bernd$$b2$$urwth
000814711 7001_ $$0P:(DE-82)IDM03380$$aBamer, Franz$$b3$$eCorresponding author$$urwth
000814711 773__ $$0PERI:(DE-600)1494583-6$$a10.1002/eqe.3432$$n8$$p2098-2114$$tEarthquake engineering & structural dynamics$$v50$$x1096-9845$$y2021
000814711 8564_ $$uhttps://publications.rwth-aachen.de/record/814711/files/814711.pdf$$yOpenAccess
000814711 8564_ $$uhttps://publications.rwth-aachen.de/record/814711/files/814711.gif?subformat=icon$$xicon$$yOpenAccess
000814711 8564_ $$uhttps://publications.rwth-aachen.de/record/814711/files/814711.jpg?subformat=icon-180$$xicon-180$$yOpenAccess
000814711 8564_ $$uhttps://publications.rwth-aachen.de/record/814711/files/814711.jpg?subformat=icon-700$$xicon-700$$yOpenAccess
000814711 909CO $$ooai:publications.rwth-aachen.de:814711$$popenaire$$popen_access$$pdriver$$pdnbdelivery$$pVDB
000814711 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-82)IDM04268$$aRWTH Aachen$$b0$$kRWTH
000814711 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-82)IDM01173$$aRWTH Aachen$$b1$$kRWTH
000814711 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-82)IDM01243$$aRWTH Aachen$$b2$$kRWTH
000814711 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-82)IDM03380$$aRWTH Aachen$$b3$$kRWTH
000814711 9141_ $$y2021
000814711 9151_ $$0StatID:(DE-HGF)0031$$2StatID$$aPeer reviewed article$$x0
000814711 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000814711 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bEARTHQ ENG STRUCT D : 2019$$d2021-01-31
000814711 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-01-31
000814711 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2021-01-31
000814711 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-01-31
000814711 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2021-01-31
000814711 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2021-01-31
000814711 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2021-01-31
000814711 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000814711 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology$$d2021-01-31
000814711 915__ $$0StatID:(DE-HGF)3001$$2StatID$$aDEAL Wiley$$d2021-01-31$$wger
000814711 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2021-01-31
000814711 9201_ $$0I:(DE-82)411110_20140620$$k411110$$lLehrstuhl und Institut für Allgemeine Mechanik$$x0
000814711 961__ $$c2021-03-01T10:33:29.576609$$x2021-03-01T10:33:29.576609$$z2021-03-01
000814711 9801_ $$aFullTexts
000814711 980__ $$aI:(DE-82)411110_20140620
000814711 980__ $$aUNRESTRICTED
000814711 980__ $$aVDB
000814711 980__ $$ajournal